TY - GEN
T1 - enpheeph
T2 - 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022
AU - Colucci, Alessio
AU - Steininger, Andreas
AU - Shafique, Muhammad
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Research on Deep Neural Networks (DNNs) has focused on improving performance and accuracy for real-world deployments, leading to new models, such as Spiking Neural Networks (SNNs), and optimization techniques, e.g., quantization and pruning for compressed networks. However, the deployment of these innovative models and optimization techniques introduces possible reliability issues, which is a pillar for DNNs to be widely used in safety-critical applications, e.g., autonomous driving. Moreover, scaling technology nodes have the associated risk of multiple faults happening at the same time, a possibility not addressed in state-of-the-art resiliency analyses. Towards better reliability analysis for DNNs, we present enpheeph, a Fault Injection Framework for Spiking and Compressed DNNs. The enpheeph framework enables optimized execution on specialized hardware devices, e.g., GPUs, while providing complete customizability to investigate different fault models, emulating various reliability constraints and use-cases. Hence, the faults can be executed on SNNs as well as compressed networks with minimal-to-none modifications to the underlying code, a feat that is not achievable by other state-of-the-art tools. To evaluate our enpheeph framework, we analyze the resiliency of different DNN and SNN models, with different compression techniques. By injecting a random and increasing number of faults, we show that DNNs can show a reduction in accuracy with a fault rate as low as 7times 10-7 faults per parameter, with an accuracy drop higher than 40%. Run-time overhead when executing enpheeph is less than 20% of the baseline execution time when executing 100 000 faults concurrently, at least 10× lower than state-of-the-art frameworks, making enpheeph future-proof for complex fault injection scenarios. We release the source code of our enpheeph framework under an open-source license at https://github.com/Alexei95/enpheeph.
AB - Research on Deep Neural Networks (DNNs) has focused on improving performance and accuracy for real-world deployments, leading to new models, such as Spiking Neural Networks (SNNs), and optimization techniques, e.g., quantization and pruning for compressed networks. However, the deployment of these innovative models and optimization techniques introduces possible reliability issues, which is a pillar for DNNs to be widely used in safety-critical applications, e.g., autonomous driving. Moreover, scaling technology nodes have the associated risk of multiple faults happening at the same time, a possibility not addressed in state-of-the-art resiliency analyses. Towards better reliability analysis for DNNs, we present enpheeph, a Fault Injection Framework for Spiking and Compressed DNNs. The enpheeph framework enables optimized execution on specialized hardware devices, e.g., GPUs, while providing complete customizability to investigate different fault models, emulating various reliability constraints and use-cases. Hence, the faults can be executed on SNNs as well as compressed networks with minimal-to-none modifications to the underlying code, a feat that is not achievable by other state-of-the-art tools. To evaluate our enpheeph framework, we analyze the resiliency of different DNN and SNN models, with different compression techniques. By injecting a random and increasing number of faults, we show that DNNs can show a reduction in accuracy with a fault rate as low as 7times 10-7 faults per parameter, with an accuracy drop higher than 40%. Run-time overhead when executing enpheeph is less than 20% of the baseline execution time when executing 100 000 faults concurrently, at least 10× lower than state-of-the-art frameworks, making enpheeph future-proof for complex fault injection scenarios. We release the source code of our enpheeph framework under an open-source license at https://github.com/Alexei95/enpheeph.
KW - Compressed Networks
KW - Deep Neural Networks
KW - Fault Injection
KW - Quantized Neural Networks
KW - Resiliency
KW - Sparse Neural Networks
KW - Spiking Neural Networks
UR - http://www.scopus.com/inward/record.url?scp=85146346675&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85146346675&partnerID=8YFLogxK
U2 - 10.1109/IROS47612.2022.9982181
DO - 10.1109/IROS47612.2022.9982181
M3 - Conference contribution
AN - SCOPUS:85146346675
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 5155
EP - 5162
BT - IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2022
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 23 October 2022 through 27 October 2022
ER -